Deep Multi-Task Learning with Adversarial-and-Cooperative Nets
Authors: Pei Yang, Qi Tan, Jieping Ye, Hanghang Tong, Jingrui He
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that our proposed method significantly outperforms the state-of-the-art algorithms on the benchmark datasets in both multi-task learning and semi-supervised domain adaptation scenarios. |
| Researcher Affiliation | Academia | Pei Yang1,2 , Qi Tan3 , Jieping Ye4 , Hanghang Tong2 and Jingrui He2 1South China University of Technology 2Arizona State University 3South China Normal University 4University of Michigan |
| Pseudocode | Yes | Algorithm 1 The TACO Algorithm |
| Open Source Code | No | The paper does not provide any links to source code or explicitly state that the code for the described methodology is available. |
| Open Datasets | Yes | The Office-Home1 [Venkateswara et al., 2017] dataset... 1http://hemanthdv.org/Office Home-Dataset/ The Office-312 [Saenko et al., 2010] dataset... 2https://people.eecs.berkeley.edu/ jhoffman/domainadapt/ The Office-Caltech dataset consists of... 3http://www.vision.caltech.edu/Image Datasets/Caltech256/ |
| Dataset Splits | Yes | We follow the standard protocol [Zhang and Yeung, 2010; Long et al., 2017a] for multi-task learning and randomly select 5%, 10%, and 20% samples from each task as trainset and use the rest as testset, respectively. A half of trainset is randomly chosen to select the optimal parameters. |
| Hardware Specification | No | The paper mentions using Alex Net and VGGnet as base networks, and being pre-trained on Image Net, but it does not specify the hardware (e.g., GPU models, CPU, memory) used for conducting their experiments. |
| Software Dependencies | No | The TACO algorithm is implemented using the Caffe framework [Jia et al., 2014]. No specific version number for Caffe or other software dependencies is provided. |
| Experiment Setup | Yes | The initial learning rate is set to 0.001, and momentum is 0.9. The training iteration is set as τmax = 1000, and batch size b = 20. We empirically set the parameter α = 0.1. |